Underdetermined blind source separation based on null-space representation and maximum likelihood - Rong-jie WANG; Yi-ju ZHAN; Hai-feng ZHOU (Tongxin xuebao)
Aiming to the estimation of source numbers,mixing mat ix and source signals under underdetermined case,and a method of underdetermined blind source separation with an unknown number of sources was proposed.Firstly,an algorithm based on S transforms and clustering technology was introduced to estimate the number of sources and mixing mixtures.Then sources were represented as null space form,the recovery of source signals usi a method based on maximum likelihood.The simulation results show that the proposed method can separate sources of super-Gaussian distribution and sub-Gaussian distribution,and compared to other conventional algorithms,estimated mixing matrix and separated sources with higher accuracy.